2020
DOI: 10.1039/d0an01638a
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Structural-based connectivity and omic phenotype evaluations (SCOPE): a cheminformatics toolbox for investigating lipidomic changes in complex systems

Abstract: Since its inception, the main goal of the lipidomics field has been to characterize lipid species and their respective biological roles. However, difficulties in both full speciation and biological interpretation...

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Cited by 20 publications
(20 citation statements)
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“…and provides a heatmap of log2 fold change ratios relative to a control. 41 This enabled a spatial comparison of each spheroid layer to further visualize the lipidomic changes at both a subclass-wide and even on a lipid species-specific level. Using the 2D monolayer data for our baseline comparison, Figure 2 illustrates all the lipid abundance alterations in the three spheroid regions.…”
Section: Untargeted Lipidomics Profiling Of 2d Monolayer Cultures And...mentioning
confidence: 99%
“…and provides a heatmap of log2 fold change ratios relative to a control. 41 This enabled a spatial comparison of each spheroid layer to further visualize the lipidomic changes at both a subclass-wide and even on a lipid species-specific level. Using the 2D monolayer data for our baseline comparison, Figure 2 illustrates all the lipid abundance alterations in the three spheroid regions.…”
Section: Untargeted Lipidomics Profiling Of 2d Monolayer Cultures And...mentioning
confidence: 99%
“…Lipidomic relationships were investigated using cheminformatics to interrogate structure-function associations across head groups and fatty acyl (FA) moieties [ 37 , 38 , 39 ]. Head group clustering was completed with the SCOPE toolbox [ 39 ].…”
Section: Methodsmentioning
confidence: 99%
“…Lipidomic relationships were investigated using cheminformatics to interrogate structure-function associations across head groups and fatty acyl (FA) moieties [ 37 , 38 , 39 ]. Head group clustering was completed with the SCOPE toolbox [ 39 ]. Here, SMILES [ 40 ] obtained from LipidMaps [ 34 ] for each lipid identification were clustered by structural similarity using an ECFP_6 fingerprint [ 41 ], Tanimoto distance and complete linkage using the fingerprint and ggtree packages in R (Version 3.6.2, Vienna, Austria) [ 42 , 43 ].…”
Section: Methodsmentioning
confidence: 99%
“…These approaches are employed to estimate adjustments in metabolites connected to lipid metabolism at variable exercise intensities and durations in body fluids such as blood and plasma [44]. A number of studies have proved that metabolomics and lipidomics may bring to light disorders in oxidative stress [45], abnormalities in energetic substrates used during physical exercise [46], and various metabolic phenotypes related to physiological parameters (i.e., VO 2max and lactate clearance capacity) [47].…”
Section: Lipidomic Analyses Of Biological Fluids Following Exercise: the Blood Rolementioning
confidence: 99%